التفاصيل البيبلوغرافية
| العنوان: |
Forcasting electric energy demand based on ARIMA (autoregressive integrated moving average) in Banyuwangi city. |
| المؤلفون: |
Sujito, Putra, Rizky Krismansyah, Faiz, Moh. Rodhi, Syah, Abdullah Iskandar, Falah, Moh. Zainul |
| المصدر: |
AIP Conference Proceedings; 2023, Vol. 2687 Issue 1, p1-9, 9p |
| مصطلحات موضوعية: |
BOX-Jenkins forecasting, MOVING average process, ENERGY consumption, LOAD forecasting (Electric power systems), CITIES & towns, DEMAND forecasting, ELECTRICAL load |
| مصطلحات جغرافية: |
PAPUA (Indonesia) |
| مستخلص: |
The current era with very advanced technology, most of which use electricity as an energy source, research is needed for the growth of electrical energy needs. One of them is in the city of Banyuwangi which is one of the cities in East Java that is experiencing rapid development in terms of the industry which automatically affects the growth of electrical energy needs. In this case, electrical load forecasting has an important role in the distribution of the electricity network to maintain a balance between demand and supply of electricity, which research has never been done in the city of Banyuwangi. Therefore, an accurate method is needed to predict the electrical load. So in this study using the Autoregressive Integrated Moving Average (ARIMA) method. The ARIMA method is the fully independent variable in making forecasts. The advantage of this method is that it can accept all types of stationary model data. This study takes medium-term load forecasting from January 2015 to December 2019 for the period 2020. From this research, the error value or MAPE will also be obtained. The beginning of data processing from this research is by looking at the time series plot data, stationary data invariance, and stationary data on average. The results of the three aspects indicate that the data is still not stationary. So it is necessary to transform the data. On the average stationery, it is necessary to make a distinction to obtain stationary data. Next, determine the ARIMA model using ACF for order q and PACF for order p. The result is 2 lags that are out of bounds from the partial image and 1 lag that is out of bounds from the autocorrelation image, because differencing is only done 1 time, the possible models are (2,1,1) (2,1,0) (1,1, 1) (1,1,0) (0,1,1) and then testing the parameters. [ABSTRACT FROM AUTHOR] |
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| قاعدة البيانات: |
Complementary Index |
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